Supporting the Understanding of Rare Disease Diagnostics with Questionnaire-Based Data Analysis and Computer-Aided Classifier Fusion
Xiaowei Zhang
Cite this publication as
Xiaowei Zhang, Supporting the Understanding of Rare Disease Diagnostics with Questionnaire-Based Data Analysis and Computer-Aided Classifier Fusion (2023), Logos Verlag, Berlin, ISBN: 9783832583491
Description / Abstract
Orphan diseases pose diagnostic challenges due to complex pathologies, limited epidemiological data, and clinical experience. The development of artificial intelligence and machine learning methods has the potential to enhance the accuracy of decision support systems, improving diagnosis outcomes for rare disease patients. This research aims to create a repository for characterizing rare diseases by collecting past experiences of diagnosed patients, reducing gaps in symptom interpretation.
This interdisciplinary study, in collaboration with medical experts, has resulted in a computer-aided diagnostic support system utilizing statistical analysis and machine learning algorithms. The system incorporates disease profile aggregation, pattern recognition, and information comparison. An interactive data visualization platform has been established to promote intuitive understanding and evaluate system diagnosis with graphics-based disease feature comparison. It supports medical practitioners during the diagnostic process by presenting visually appealing information. The patient-oriented inquiry mechanism efficiently reduces unnecessary questions while providing a reliable diagnosis based on probability. By combining statistical learning with the visualization module, the system can discover disease-related symptom patterns, offering new means for diagnosing rare disorders. The supplementary diagnosis prediction mechanism can be applied effectively to analyze different groups in surveyswith closed-ended questions.
This interdisciplinary study, in collaboration with medical experts, has resulted in a computer-aided diagnostic support system utilizing statistical analysis and machine learning algorithms. The system incorporates disease profile aggregation, pattern recognition, and information comparison. An interactive data visualization platform has been established to promote intuitive understanding and evaluate system diagnosis with graphics-based disease feature comparison. It supports medical practitioners during the diagnostic process by presenting visually appealing information. The patient-oriented inquiry mechanism efficiently reduces unnecessary questions while providing a reliable diagnosis based on probability. By combining statistical learning with the visualization module, the system can discover disease-related symptom patterns, offering new means for diagnosing rare disorders. The supplementary diagnosis prediction mechanism can be applied effectively to analyze different groups in surveyswith closed-ended questions.
Table of content
- BEGINN
- Contents
- 1 Introduction
- 1.1 Study Background and Motivation
- 1.2 Research Questions and Objectives
- 1.3 Conceptual Architecture Design
- 1.4 Research Achievements and Contribution
- 1.5 Structure of the Thesis
- 2 Fundamentals and RelatedWork
- 2.1 Statistics in Diagnostic Medicine
- 2.2 Supervised ML Techniques
- 2.3 Classification Model Evaluation and Selection
- 2.4 Ensemble Methods for Classifiers
- 2.5 Probability Calibration Methods
- 2.6 Data Visualization
- 3 Empirically-Based Disease Prediction Model
- 3.1 System Diagnostic Procedure
- 3.2 Classification Model Selection and Calibration
- 3.3 Ensemble Method for Binary Classification Tasks
- 3.4 Probability Inspection Table for Local Validation
- 3.5 Validation of the Classification Model
- 3.6 Feasibility of the Disease Classification Model
- 4 Interactive Visual Inspection of Research Findings
- 4.1 Symptom Patterns of Different Rare Disease Groups
- 4.2 System Diagnosis Distribution with Heatmaps
- 4.3 Advantages of Diagnostic Data Visualization
- 5 Dynamic Adaptive Decision-Making Mechanism
- 5.1 Dimension Reduction Analysis
- 5.2 Dynamic Adaptive Questioning Concept
- 5.3 Validating the Dynamic Adaptive Questioning Method
- 5.4 Discussion of Dynamic Diagnosing Method
- 6 Discussion and Conclusion
- 6.1 Discussion of the Research Findings
- 6.2 Research Limitations
- 6.3 Research Prospects and FutureWork
- 6.4 Summary of the Research
- Appendices
- A Rare Disease Data Acquisition
- B Data Storage Structure
- C The Diagnosis–Question Correlation Ranking
- D Publications in the Medical Field
- E Questionnaire Content
- Bibliography
- Curriculum Vitae
- List of Scientific Publications